We empirically evaluated the fault localization performance of UniVal in a substantial empirical study involving subject programs from the latest, expanded version (2.0.0) of the popular Defects4J evaluation framework [1]}. We compared the fault localization costs of UniVal and several competing techniques: the non-interventional value-based techniques Elastic Predicates (ESP) [2]} and NUMFL (specifically the two variants NUMFL-DLRM and NUMFL-QRM) [3]}; the non-interventional coverage-based technique of Baah et al. [4]}, which employs linear regression for causal inference; the interventional technique Predicate Switching [5]}, which alters conditional branching; and finally two well-known coverage-based SFL (CB-SFL) metrics that performed well in recent comparative studies [6]}, [7]}, [8]}, namely, Ochiai [6]} and D-Star (with star \(=2\) ) [10]}.
